Overview

Dataset statistics

Number of variables9
Number of observations400
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.2 KiB
Average record size in memory72.3 B

Variable types

NUM8
BOOL1

Warnings

Serial No. has unique values Unique

Reproduction

Analysis started2020-12-29 23:07:58.993781
Analysis finished2020-12-29 23:08:13.033951
Duration14.04 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Serial No.
Real number (ℝ≥0)

UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.5
Minimum1
Maximum400
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-29T18:08:13.129972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20.95
Q1100.75
median200.5
Q3300.25
95-th percentile380.05
Maximum400
Range399
Interquartile range (IQR)199.5

Descriptive statistics

Standard deviation115.6143013
Coefficient of variation (CV)0.5766299317
Kurtosis-1.2
Mean200.5
Median Absolute Deviation (MAD)100
Skewness0
Sum80200
Variance13366.66667
MonotocityStrictly increasing
2020-12-29T18:08:13.281007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
40010.2%
 
13710.2%
 
12710.2%
 
12810.2%
 
12910.2%
 
13010.2%
 
13110.2%
 
13210.2%
 
13310.2%
 
13410.2%
 
Other values (390)39097.5%
 
ValueCountFrequency (%) 
110.2%
 
210.2%
 
310.2%
 
410.2%
 
510.2%
 
ValueCountFrequency (%) 
40010.2%
 
39910.2%
 
39810.2%
 
39710.2%
 
39610.2%
 

GRE Score
Real number (ℝ≥0)

Distinct49
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean316.8075
Minimum290
Maximum340
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-29T18:08:13.443043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile298
Q1308
median317
Q3325
95-th percentile336
Maximum340
Range50
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.47364611
Coefficient of variation (CV)0.03621645988
Kurtosis-0.700489457
Mean316.8075
Median Absolute Deviation (MAD)8
Skewness-0.06289345936
Sum126723
Variance131.6445551
MonotocityNot monotonic
2020-12-29T18:08:13.596078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%) 
324194.8%
 
312194.8%
 
316143.5%
 
322143.5%
 
314143.5%
 
321133.2%
 
325133.2%
 
311123.0%
 
317123.0%
 
320123.0%
 
Other values (39)25864.5%
 
ValueCountFrequency (%) 
29020.5%
 
29310.2%
 
29420.5%
 
29541.0%
 
29651.2%
 
ValueCountFrequency (%) 
34082.0%
 
33930.8%
 
33841.0%
 
33710.2%
 
33651.2%
 

TOEFL Score
Real number (ℝ≥0)

Distinct29
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.41
Minimum92
Maximum120
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-29T18:08:13.739110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum92
5-th percentile98
Q1103
median107
Q3112
95-th percentile118
Maximum120
Range28
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.069513777
Coefficient of variation (CV)0.05650790222
Kurtosis-0.5787784114
Mean107.41
Median Absolute Deviation (MAD)4
Skewness0.0572159137
Sum42964
Variance36.83899749
MonotocityNot monotonic
2020-12-29T18:08:13.854136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%) 
110379.2%
 
105287.0%
 
107266.5%
 
104256.2%
 
106246.0%
 
112215.2%
 
100194.8%
 
99184.5%
 
111174.2%
 
109174.2%
 
Other values (19)16842.0%
 
ValueCountFrequency (%) 
9210.2%
 
9320.5%
 
9410.2%
 
9520.5%
 
9641.0%
 
ValueCountFrequency (%) 
12082.0%
 
11992.2%
 
11892.2%
 
11771.8%
 
116112.8%
 

University Rating
Real number (ℝ≥0)

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0875
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-29T18:08:13.977164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.14372813
Coefficient of variation (CV)0.3704382607
Kurtosis-0.7962869645
Mean3.0875
Median Absolute Deviation (MAD)1
Skewness0.1712602774
Sum1235
Variance1.308114035
MonotocityNot monotonic
2020-12-29T18:08:14.085188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
313333.2%
 
210726.8%
 
47418.5%
 
56015.0%
 
1266.5%
 
ValueCountFrequency (%) 
1266.5%
 
210726.8%
 
313333.2%
 
47418.5%
 
56015.0%
 
ValueCountFrequency (%) 
56015.0%
 
47418.5%
 
313333.2%
 
210726.8%
 
1266.5%
 

SOP
Real number (ℝ≥0)

Distinct9
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-29T18:08:14.210216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q12.5
median3.5
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.006868641
Coefficient of variation (CV)0.2961378357
Kurtosis-0.6756103428
Mean3.4
Median Absolute Deviation (MAD)0.5
Skewness-0.2757611681
Sum1360
Variance1.013784461
MonotocityNot monotonic
2020-12-29T18:08:14.323242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
3.57017.5%
 
47017.5%
 
36416.0%
 
4.55313.2%
 
2.54711.8%
 
5379.2%
 
2338.2%
 
1.5205.0%
 
161.5%
 
ValueCountFrequency (%) 
161.5%
 
1.5205.0%
 
2338.2%
 
2.54711.8%
 
36416.0%
 
ValueCountFrequency (%) 
5379.2%
 
4.55313.2%
 
47017.5%
 
3.57017.5%
 
36416.0%
 

LOR
Real number (ℝ≥0)

Distinct9
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4525
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-29T18:08:14.453271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3.5
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8984775483
Coefficient of variation (CV)0.2602396954
Kurtosis-0.6624841166
Mean3.4525
Median Absolute Deviation (MAD)0.5
Skewness-0.1069914787
Sum1381
Variance0.8072619048
MonotocityNot monotonic
2020-12-29T18:08:14.569297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
38521.2%
 
47719.2%
 
3.57318.2%
 
4.54511.2%
 
2.5399.8%
 
2389.5%
 
5358.8%
 
1.571.8%
 
110.2%
 
ValueCountFrequency (%) 
110.2%
 
1.571.8%
 
2389.5%
 
2.5399.8%
 
38521.2%
 
ValueCountFrequency (%) 
5358.8%
 
4.54511.2%
 
47719.2%
 
3.57318.2%
 
38521.2%
 

CGPA
Real number (ℝ≥0)

Distinct168
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.598925
Minimum6.8
Maximum9.92
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-29T18:08:14.719331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6.8
5-th percentile7.64
Q18.17
median8.61
Q39.0625
95-th percentile9.601
Maximum9.92
Range3.12
Interquartile range (IQR)0.8925

Descriptive statistics

Standard deviation0.5963170965
Coefficient of variation (CV)0.06934786575
Kurtosis-0.4584756257
Mean8.598925
Median Absolute Deviation (MAD)0.45
Skewness-0.06599054378
Sum3439.57
Variance0.3555940796
MonotocityNot monotonic
2020-12-29T18:08:14.855362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
892.2%
 
8.7682.0%
 
8.5671.8%
 
8.4561.5%
 
8.6461.5%
 
9.1161.5%
 
8.251.2%
 
8.851.2%
 
8.5451.2%
 
8.6551.2%
 
Other values (158)33884.5%
 
ValueCountFrequency (%) 
6.810.2%
 
7.210.2%
 
7.2510.2%
 
7.2810.2%
 
7.310.2%
 
ValueCountFrequency (%) 
9.9210.2%
 
9.9110.2%
 
9.8710.2%
 
9.8210.2%
 
9.830.8%
 

Research
Boolean

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1
219 
0
181 
ValueCountFrequency (%) 
121954.8%
 
018145.2%
 
2020-12-29T18:08:14.957385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Chance of Admit
Real number (ℝ≥0)

Distinct60
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72435
Minimum0.34
Maximum0.97
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-29T18:08:15.056407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.46
Q10.64
median0.73
Q30.83
95-th percentile0.94
Maximum0.97
Range0.63
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.1426093302
Coefficient of variation (CV)0.1968790366
Kurtosis-0.3891259175
Mean0.72435
Median Absolute Deviation (MAD)0.09
Skewness-0.3534480999
Sum289.74
Variance0.02033742105
MonotocityNot monotonic
2020-12-29T18:08:15.195439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.64174.2%
 
0.71164.0%
 
0.72153.8%
 
0.73133.2%
 
0.78123.0%
 
0.76123.0%
 
0.94123.0%
 
0.79123.0%
 
0.7123.0%
 
0.74112.8%
 
Other values (50)26867.0%
 
ValueCountFrequency (%) 
0.3420.5%
 
0.3620.5%
 
0.3820.5%
 
0.3910.2%
 
0.4230.8%
 
ValueCountFrequency (%) 
0.9741.0%
 
0.9671.8%
 
0.9541.0%
 
0.94123.0%
 
0.9392.2%
 

Interactions

2020-12-29T18:08:03.059703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:03.227741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:03.371774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:03.515806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:03.673842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:03.827878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:03.981911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:04.128944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:04.394004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:04.532035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:04.658064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:04.785092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:04.928125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:05.066155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:05.205187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:05.335216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:05.467246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:05.607278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:05.735306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:05.862335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:06.004367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:06.144399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:06.282430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:06.411458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:06.543488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:06.699524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:06.843556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:06.988589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:07.145623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:07.302660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:07.458695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:07.606728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:07.758762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:07.913797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:08.056830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:08.198861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:08.354897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:08.508932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:08.662966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:08.808999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:08.956032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:09.111067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:09.254099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:09.396131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:09.552166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:09.707201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:09.861237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:10.007269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:10.156303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:10.302335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:10.434365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:10.567395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:10.715429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:10.859461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:11.005494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:11.143525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:11.424588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:11.570621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:11.704652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:11.836681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:11.986715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:12.135749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:12.285782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:12.425813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-12-29T18:08:15.328468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-29T18:08:15.536515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-29T18:08:15.764566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-29T18:08:15.972613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-29T18:08:12.662868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-29T18:08:12.921925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
0133711844.54.59.6510.92
1232410744.04.58.8710.76
2331610433.03.58.0010.72
3432211033.52.58.6710.80
4531410322.03.08.2100.65
5633011554.53.09.3410.90
6732110933.04.08.2010.75
7830810123.04.07.9000.68
8930210212.01.58.0000.50
91032310833.53.08.6000.45

Last rows

Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
39039131410222.02.58.2400.64
39139231810632.03.08.6500.71
39239332611244.03.59.1210.84
39339431710423.03.08.7600.77
39439532911144.54.09.2310.89
39539632411033.53.59.0410.82
39639732510733.03.59.1110.84
39739833011645.04.59.4510.91
39839931210333.54.08.7800.67
39940033311745.04.09.6610.95